Overview

Dataset statistics

Number of variables25
Number of observations844392
Missing cells1809345
Missing cells (%)8.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory360.8 MiB
Average record size in memory448.1 B

Variable types

Numeric14
DateTime1
Categorical10

Alerts

Open has constant value "1"Constant
Sales is highly overall correlated with CustomersHigh correlation
Customers is highly overall correlated with SalesHigh correlation
CompetitionOpenSinceYear is highly overall correlated with CompetitionOpenHigh correlation
Promo2SinceWeek is highly overall correlated with Promo2 and 1 other fieldsHigh correlation
Promo2SinceYear is highly overall correlated with Promo2Open and 1 other fieldsHigh correlation
Month is highly overall correlated with WeekOfYearHigh correlation
WeekOfYear is highly overall correlated with MonthHigh correlation
CompetitionOpen is highly overall correlated with CompetitionOpenSinceYearHigh correlation
Promo2Open is highly overall correlated with Promo2SinceYear and 1 other fieldsHigh correlation
StoreType is highly overall correlated with AssortmentHigh correlation
Assortment is highly overall correlated with StoreTypeHigh correlation
Promo2 is highly overall correlated with Promo2SinceWeek and 3 other fieldsHigh correlation
PromoInterval is highly overall correlated with Promo2SinceWeek and 1 other fieldsHigh correlation
StateHoliday is highly imbalanced (99.3%)Imbalance
CompetitionOpenSinceMonth has 268619 (31.8%) missing valuesMissing
CompetitionOpenSinceYear has 268619 (31.8%) missing valuesMissing
Promo2SinceWeek has 423307 (50.1%) missing valuesMissing
Promo2SinceYear has 423307 (50.1%) missing valuesMissing
PromoInterval has 423307 (50.1%) missing valuesMissing
CompetitionOpen has 343310 (40.7%) zerosZeros
Promo2Open has 481933 (57.1%) zerosZeros

Reproduction

Analysis started2024-01-22 12:33:53.267926
Analysis finished2024-01-22 12:36:50.546534
Duration2 minutes and 57.28 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Store
Real number (ℝ)

Distinct1115
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean558.42292
Minimum1
Maximum1115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-01-22T18:06:50.797045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile56
Q1280
median558
Q3837
95-th percentile1060
Maximum1115
Range1114
Interquartile range (IQR)557

Descriptive statistics

Standard deviation321.73191
Coefficient of variation (CV)0.57614382
Kurtosis-1.1988414
Mean558.42292
Median Absolute Deviation (MAD)278
Skewness0.00041375446
Sum4.7152785 × 108
Variance103511.42
MonotonicityNot monotonic
2024-01-22T18:06:51.027040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
562 942
 
0.1%
769 942
 
0.1%
733 942
 
0.1%
423 942
 
0.1%
85 942
 
0.1%
262 942
 
0.1%
335 942
 
0.1%
682 942
 
0.1%
1097 942
 
0.1%
494 942
 
0.1%
Other values (1105) 834972
98.9%
ValueCountFrequency (%)
1 781
0.1%
2 784
0.1%
3 779
0.1%
4 784
0.1%
5 779
0.1%
6 780
0.1%
7 786
0.1%
8 784
0.1%
9 779
0.1%
10 784
0.1%
ValueCountFrequency (%)
1115 781
0.1%
1114 784
0.1%
1113 784
0.1%
1112 779
0.1%
1111 779
0.1%
1110 783
0.1%
1109 622
0.1%
1108 780
0.1%
1107 623
0.1%
1106 784
0.1%

DayOfWeek
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5203614
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-01-22T18:06:51.186231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7236892
Coefficient of variation (CV)0.48963417
Kurtosis-1.2593101
Mean3.5203614
Median Absolute Deviation (MAD)2
Skewness0.01929954
Sum2972565
Variance2.9711045
MonotonicityNot monotonic
2024-01-22T18:06:51.374899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 144058
17.1%
2 143961
17.0%
3 141936
16.8%
5 138640
16.4%
1 137560
16.3%
4 134644
15.9%
7 3593
 
0.4%
ValueCountFrequency (%)
1 137560
16.3%
2 143961
17.0%
3 141936
16.8%
4 134644
15.9%
5 138640
16.4%
6 144058
17.1%
7 3593
 
0.4%
ValueCountFrequency (%)
7 3593
 
0.4%
6 144058
17.1%
5 138640
16.4%
4 134644
15.9%
3 141936
16.8%
2 143961
17.0%
1 137560
16.3%

Date
Date

Distinct942
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
Minimum2013-01-01 00:00:00
Maximum2015-07-31 00:00:00
2024-01-22T18:06:51.573201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:51.789622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Sales
Real number (ℝ)

Distinct21734
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6955.5143
Minimum0
Maximum41551
Zeros54
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-01-22T18:06:52.035423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3173
Q14859
median6369
Q38360
95-th percentile12668
Maximum41551
Range41551
Interquartile range (IQR)3501

Descriptive statistics

Standard deviation3104.2147
Coefficient of variation (CV)0.44629549
Kurtosis4.8520115
Mean6955.5143
Median Absolute Deviation (MAD)1694
Skewness1.593922
Sum5.8731806 × 109
Variance9636148.8
MonotonicityNot monotonic
2024-01-22T18:06:52.221342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5674 215
 
< 0.1%
5558 197
 
< 0.1%
5483 196
 
< 0.1%
6049 195
 
< 0.1%
6214 195
 
< 0.1%
5723 194
 
< 0.1%
5449 192
 
< 0.1%
5489 191
 
< 0.1%
5140 191
 
< 0.1%
5041 190
 
< 0.1%
Other values (21724) 842436
99.8%
ValueCountFrequency (%)
0 54
< 0.1%
46 1
 
< 0.1%
124 1
 
< 0.1%
133 1
 
< 0.1%
286 1
 
< 0.1%
297 1
 
< 0.1%
316 1
 
< 0.1%
416 1
 
< 0.1%
506 1
 
< 0.1%
520 1
 
< 0.1%
ValueCountFrequency (%)
41551 1
< 0.1%
38722 1
< 0.1%
38484 1
< 0.1%
38367 1
< 0.1%
38037 1
< 0.1%
38025 1
< 0.1%
37646 1
< 0.1%
37403 1
< 0.1%
37376 1
< 0.1%
37122 1
< 0.1%

Customers
Real number (ℝ)

Distinct4086
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean762.7284
Minimum0
Maximum7388
Zeros52
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-01-22T18:06:52.434450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile349
Q1519
median676
Q3893
95-th percentile1440
Maximum7388
Range7388
Interquartile range (IQR)374

Descriptive statistics

Standard deviation401.22767
Coefficient of variation (CV)0.52604266
Kurtosis13.313755
Mean762.7284
Median Absolute Deviation (MAD)179
Skewness2.7881104
Sum6.4404176 × 108
Variance160983.65
MonotonicityNot monotonic
2024-01-22T18:06:52.620873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
560 2414
 
0.3%
576 2363
 
0.3%
603 2337
 
0.3%
571 2330
 
0.3%
555 2328
 
0.3%
566 2327
 
0.3%
517 2326
 
0.3%
539 2309
 
0.3%
651 2299
 
0.3%
533 2298
 
0.3%
Other values (4076) 821061
97.2%
ValueCountFrequency (%)
0 52
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
8 1
 
< 0.1%
13 1
 
< 0.1%
18 1
 
< 0.1%
36 1
 
< 0.1%
40 1
 
< 0.1%
44 1
 
< 0.1%
50 1
 
< 0.1%
ValueCountFrequency (%)
7388 1
< 0.1%
5494 1
< 0.1%
5458 1
< 0.1%
5387 1
< 0.1%
5297 1
< 0.1%
5192 1
< 0.1%
5152 1
< 0.1%
5145 1
< 0.1%
5132 1
< 0.1%
5112 1
< 0.1%

Open
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.1 MiB
1
844392 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters844392
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 844392
100.0%

Length

2024-01-22T18:06:52.846241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-22T18:06:53.037446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 844392
100.0%

Most occurring characters

ValueCountFrequency (%)
1 844392
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 844392
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 844392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 844392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 844392
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 844392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 844392
100.0%

Promo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.1 MiB
0
467496 
1
376896 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters844392
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 467496
55.4%
1 376896
44.6%

Length

2024-01-22T18:06:53.185980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-22T18:06:53.384604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 467496
55.4%
1 376896
44.6%

Most occurring characters

ValueCountFrequency (%)
0 467496
55.4%
1 376896
44.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 844392
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 467496
55.4%
1 376896
44.6%

Most occurring scripts

ValueCountFrequency (%)
Common 844392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 467496
55.4%
1 376896
44.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 844392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 467496
55.4%
1 376896
44.6%

StateHoliday
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.4 MiB
NotHoliday
843482 
a
 
694
b
 
145
c
 
71

Length

Max length10
Median length10
Mean length9.9903007
Min length1

Characters and Unicode

Total characters8435730
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNotHoliday
2nd rowNotHoliday
3rd rowNotHoliday
4th rowNotHoliday
5th rowNotHoliday

Common Values

ValueCountFrequency (%)
NotHoliday 843482
99.9%
a 694
 
0.1%
b 145
 
< 0.1%
c 71
 
< 0.1%

Length

2024-01-22T18:06:53.524223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-22T18:06:53.708847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
notholiday 843482
99.9%
a 694
 
0.1%
b 145
 
< 0.1%
c 71
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 1686964
20.0%
a 844176
10.0%
N 843482
10.0%
t 843482
10.0%
H 843482
10.0%
l 843482
10.0%
i 843482
10.0%
d 843482
10.0%
y 843482
10.0%
b 145
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6748766
80.0%
Uppercase Letter 1686964
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1686964
25.0%
a 844176
12.5%
t 843482
12.5%
l 843482
12.5%
i 843482
12.5%
d 843482
12.5%
y 843482
12.5%
b 145
 
< 0.1%
c 71
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 843482
50.0%
H 843482
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8435730
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1686964
20.0%
a 844176
10.0%
N 843482
10.0%
t 843482
10.0%
H 843482
10.0%
l 843482
10.0%
i 843482
10.0%
d 843482
10.0%
y 843482
10.0%
b 145
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8435730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1686964
20.0%
a 844176
10.0%
N 843482
10.0%
t 843482
10.0%
H 843482
10.0%
l 843482
10.0%
i 843482
10.0%
d 843482
10.0%
y 843482
10.0%
b 145
 
< 0.1%

SchoolHoliday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.1 MiB
0
680935 
1
163457 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters844392
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 680935
80.6%
1 163457
 
19.4%

Length

2024-01-22T18:06:53.862620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-22T18:06:54.036869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 680935
80.6%
1 163457
 
19.4%

Most occurring characters

ValueCountFrequency (%)
0 680935
80.6%
1 163457
 
19.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 844392
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 680935
80.6%
1 163457
 
19.4%

Most occurring scripts

ValueCountFrequency (%)
Common 844392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 680935
80.6%
1 163457
 
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 844392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 680935
80.6%
1 163457
 
19.4%

StoreType
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.1 MiB
a
457077 
d
258774 
c
112978 
b
 
15563

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters844392
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowc
2nd rowa
3rd rowa
4th rowc
5th rowa

Common Values

ValueCountFrequency (%)
a 457077
54.1%
d 258774
30.6%
c 112978
 
13.4%
b 15563
 
1.8%

Length

2024-01-22T18:06:54.176970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-22T18:06:54.348170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
a 457077
54.1%
d 258774
30.6%
c 112978
 
13.4%
b 15563
 
1.8%

Most occurring characters

ValueCountFrequency (%)
a 457077
54.1%
d 258774
30.6%
c 112978
 
13.4%
b 15563
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 844392
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 457077
54.1%
d 258774
30.6%
c 112978
 
13.4%
b 15563
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 844392
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 457077
54.1%
d 258774
30.6%
c 112978
 
13.4%
b 15563
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 844392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 457077
54.1%
d 258774
30.6%
c 112978
 
13.4%
b 15563
 
1.8%

Assortment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.1 MiB
a
444909 
c
391271 
b
 
8212

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters844392
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowa
2nd rowa
3rd rowa
4th rowc
5th rowa

Common Values

ValueCountFrequency (%)
a 444909
52.7%
c 391271
46.3%
b 8212
 
1.0%

Length

2024-01-22T18:06:54.503947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-22T18:06:54.676270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
a 444909
52.7%
c 391271
46.3%
b 8212
 
1.0%

Most occurring characters

ValueCountFrequency (%)
a 444909
52.7%
c 391271
46.3%
b 8212
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 844392
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 444909
52.7%
c 391271
46.3%
b 8212
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 844392
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 444909
52.7%
c 391271
46.3%
b 8212
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 844392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 444909
52.7%
c 391271
46.3%
b 8212
 
1.0%

CompetitionDistance
Real number (ℝ)

Distinct654
Distinct (%)0.1%
Missing2186
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean5457.9796
Minimum20
Maximum75860
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-01-22T18:06:54.869243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile130
Q1710
median2320
Q36890
95-th percentile20390
Maximum75860
Range75840
Interquartile range (IQR)6180

Descriptive statistics

Standard deviation7809.4373
Coefficient of variation (CV)1.4308293
Kurtosis13.413381
Mean5457.9796
Median Absolute Deviation (MAD)1970
Skewness2.9751106
Sum4.5967432 × 109
Variance60987311
MonotonicityNot monotonic
2024-01-22T18:06:55.059248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250 9210
 
1.1%
50 6249
 
0.7%
350 6239
 
0.7%
1200 6072
 
0.7%
190 6066
 
0.7%
90 5609
 
0.7%
180 5422
 
0.6%
150 5294
 
0.6%
330 5294
 
0.6%
140 4684
 
0.6%
Other values (644) 782067
92.6%
ValueCountFrequency (%)
20 779
 
0.1%
30 3116
0.4%
40 3890
0.5%
50 6249
0.7%
60 2342
 
0.3%
70 3736
0.4%
80 2331
 
0.3%
90 5609
0.7%
100 3900
0.5%
110 4516
0.5%
ValueCountFrequency (%)
75860 887
0.1%
58260 885
0.1%
48330 784
0.1%
46590 784
0.1%
45740 780
0.1%
44320 780
0.1%
40860 881
0.1%
40540 780
0.1%
38710 784
0.1%
38630 882
0.1%

CompetitionOpenSinceMonth
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing268619
Missing (%)31.8%
Infinite0
Infinite (%)0.0%
Mean7.2248786
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-01-22T18:06:55.249918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median8
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2101438
Coefficient of variation (CV)0.44431803
Kurtosis-1.247663
Mean7.2248786
Median Absolute Deviation (MAD)3
Skewness-0.17157646
Sum4159890
Variance10.305023
MonotonicityNot monotonic
2024-01-22T18:06:55.388151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 95467
 
11.3%
4 72256
 
8.6%
11 70032
 
8.3%
3 52685
 
6.2%
7 49009
 
5.8%
12 47887
 
5.7%
10 46198
 
5.5%
6 37759
 
4.5%
5 32862
 
3.9%
2 31360
 
3.7%
Other values (2) 40258
 
4.8%
(Missing) 268619
31.8%
ValueCountFrequency (%)
1 10297
 
1.2%
2 31360
 
3.7%
3 52685
6.2%
4 72256
8.6%
5 32862
 
3.9%
6 37759
 
4.5%
7 49009
5.8%
8 29961
 
3.5%
9 95467
11.3%
10 46198
5.5%
ValueCountFrequency (%)
12 47887
5.7%
11 70032
8.3%
10 46198
5.5%
9 95467
11.3%
8 29961
 
3.5%
7 49009
5.8%
6 37759
 
4.5%
5 32862
 
3.9%
4 72256
8.6%
3 52685
6.2%

CompetitionOpenSinceYear
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)< 0.1%
Missing268619
Missing (%)31.8%
Infinite0
Infinite (%)0.0%
Mean2008.6977
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-01-22T18:06:56.002827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile2001
Q12006
median2010
Q32013
95-th percentile2015
Maximum2015
Range115
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.9780481
Coefficient of variation (CV)0.0029760815
Kurtosis121.84638
Mean2008.6977
Median Absolute Deviation (MAD)3
Skewness-7.5221054
Sum1.1565539 × 109
Variance35.73706
MonotonicityNot monotonic
2024-01-22T18:06:56.172256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2013 63108
 
7.5%
2012 61719
 
7.3%
2014 52815
 
6.3%
2005 46705
 
5.5%
2010 42716
 
5.1%
2011 41366
 
4.9%
2009 40713
 
4.8%
2008 40198
 
4.8%
2007 36131
 
4.3%
2006 35543
 
4.2%
Other values (13) 114759
13.6%
(Missing) 268619
31.8%
ValueCountFrequency (%)
1900 622
 
0.1%
1961 779
 
0.1%
1990 3887
 
0.5%
1994 1552
 
0.2%
1995 1404
 
0.2%
1998 766
 
0.1%
1999 6213
 
0.7%
2000 7631
 
0.9%
2001 12157
1.4%
2002 20736
2.5%
ValueCountFrequency (%)
2015 28844
3.4%
2014 52815
6.3%
2013 63108
7.5%
2012 61719
7.3%
2011 41366
4.9%
2010 42716
5.1%
2009 40713
4.8%
2008 40198
4.8%
2007 36131
4.3%
2006 35543
4.2%

Promo2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.1 MiB
0
423307 
1
421085 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters844392
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 423307
50.1%
1 421085
49.9%

Length

2024-01-22T18:06:56.338762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-22T18:06:56.500555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 423307
50.1%
1 421085
49.9%

Most occurring characters

ValueCountFrequency (%)
0 423307
50.1%
1 421085
49.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 844392
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 423307
50.1%
1 421085
49.9%

Most occurring scripts

ValueCountFrequency (%)
Common 844392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 423307
50.1%
1 421085
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 844392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 423307
50.1%
1 421085
49.9%

Promo2SinceWeek
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)< 0.1%
Missing423307
Missing (%)50.1%
Infinite0
Infinite (%)0.0%
Mean23.253426
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-01-22T18:06:56.636996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q113
median22
Q337
95-th percentile45
Maximum50
Range49
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.100569
Coefficient of variation (CV)0.60638671
Kurtosis-1.3690876
Mean23.253426
Median Absolute Deviation (MAD)13
Skewness0.10641224
Sum9791669
Variance198.82603
MonotonicityNot monotonic
2024-01-22T18:06:56.794192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
14 60541
 
7.2%
40 51507
 
6.1%
31 33238
 
3.9%
10 32214
 
3.8%
5 29722
 
3.5%
37 27116
 
3.2%
1 26873
 
3.2%
13 24579
 
2.9%
45 24072
 
2.9%
22 23645
 
2.8%
Other values (14) 87578
 
10.4%
(Missing) 423307
50.1%
ValueCountFrequency (%)
1 26873
3.2%
5 29722
3.5%
6 771
 
0.1%
9 10293
 
1.2%
10 32214
3.8%
13 24579
2.9%
14 60541
7.2%
18 22456
 
2.7%
22 23645
 
2.8%
23 3558
 
0.4%
ValueCountFrequency (%)
50 780
 
0.1%
49 622
 
0.1%
48 7033
 
0.8%
45 24072
2.9%
44 2182
 
0.3%
40 51507
6.1%
39 3889
 
0.5%
37 27116
3.2%
36 7620
 
0.9%
35 18888
 
2.2%

Promo2SinceYear
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing423307
Missing (%)50.1%
Infinite0
Infinite (%)0.0%
Mean2011.754
Minimum2009
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-01-22T18:06:56.941729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2009
Q12011
median2012
Q32013
95-th percentile2014
Maximum2015
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6609621
Coefficient of variation (CV)0.00082562883
Kurtosis-1.03757
Mean2011.754
Median Absolute Deviation (MAD)1
Skewness-0.12274344
Sum8.4711944 × 108
Variance2.7587952
MonotonicityNot monotonic
2024-01-22T18:06:57.064308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2011 95040
 
11.3%
2013 91866
 
10.9%
2014 65768
 
7.8%
2012 60716
 
7.2%
2009 53826
 
6.4%
2010 46414
 
5.5%
2015 7455
 
0.9%
(Missing) 423307
50.1%
ValueCountFrequency (%)
2009 53826
6.4%
2010 46414
5.5%
2011 95040
11.3%
2012 60716
7.2%
2013 91866
10.9%
2014 65768
7.8%
2015 7455
 
0.9%
ValueCountFrequency (%)
2015 7455
 
0.9%
2014 65768
7.8%
2013 91866
10.9%
2012 60716
7.2%
2011 95040
11.3%
2010 46414
5.5%
2009 53826
6.4%

PromoInterval
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing423307
Missing (%)50.1%
Memory size48.4 MiB
Jan,Apr,Jul,Oct
242411 
Feb,May,Aug,Nov
98005 
Mar,Jun,Sept,Dec
80669 

Length

Max length16
Median length15
Mean length15.191574
Min length15

Characters and Unicode

Total characters6396944
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJan,Apr,Jul,Oct
2nd rowJan,Apr,Jul,Oct
3rd rowJan,Apr,Jul,Oct
4th rowJan,Apr,Jul,Oct
5th rowFeb,May,Aug,Nov

Common Values

ValueCountFrequency (%)
Jan,Apr,Jul,Oct 242411
28.7%
Feb,May,Aug,Nov 98005
 
11.6%
Mar,Jun,Sept,Dec 80669
 
9.6%
(Missing) 423307
50.1%

Length

2024-01-22T18:06:57.223121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-22T18:06:57.415646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
jan,apr,jul,oct 242411
57.6%
feb,may,aug,nov 98005
23.3%
mar,jun,sept,dec 80669
 
19.2%

Most occurring characters

ValueCountFrequency (%)
, 1263255
19.7%
J 565491
 
8.8%
u 421085
 
6.6%
a 421085
 
6.6%
A 340416
 
5.3%
c 323080
 
5.1%
t 323080
 
5.1%
r 323080
 
5.1%
p 323080
 
5.1%
n 323080
 
5.1%
Other values (13) 1770212
27.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3449349
53.9%
Uppercase Letter 1684340
26.3%
Other Punctuation 1263255
 
19.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 421085
12.2%
a 421085
12.2%
c 323080
9.4%
t 323080
9.4%
r 323080
9.4%
p 323080
9.4%
n 323080
9.4%
e 259343
7.5%
l 242411
7.0%
b 98005
 
2.8%
Other values (4) 392020
11.4%
Uppercase Letter
ValueCountFrequency (%)
J 565491
33.6%
A 340416
20.2%
O 242411
14.4%
M 178674
 
10.6%
F 98005
 
5.8%
N 98005
 
5.8%
S 80669
 
4.8%
D 80669
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 1263255
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5133689
80.3%
Common 1263255
 
19.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 565491
 
11.0%
u 421085
 
8.2%
a 421085
 
8.2%
A 340416
 
6.6%
c 323080
 
6.3%
t 323080
 
6.3%
r 323080
 
6.3%
p 323080
 
6.3%
n 323080
 
6.3%
e 259343
 
5.1%
Other values (12) 1510869
29.4%
Common
ValueCountFrequency (%)
, 1263255
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6396944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 1263255
19.7%
J 565491
 
8.8%
u 421085
 
6.6%
a 421085
 
6.6%
A 340416
 
5.3%
c 323080
 
5.1%
t 323080
 
5.1%
r 323080
 
5.1%
p 323080
 
5.1%
n 323080
 
5.1%
Other values (13) 1770212
27.7%

Year
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.6 MiB
2013
337943 
2014
310417 
2015
196032 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3377568
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2013 337943
40.0%
2014 310417
36.8%
2015 196032
23.2%

Length

2024-01-22T18:06:57.583869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-22T18:06:57.755150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2013 337943
40.0%
2014 310417
36.8%
2015 196032
23.2%

Most occurring characters

ValueCountFrequency (%)
2 844392
25.0%
0 844392
25.0%
1 844392
25.0%
3 337943
10.0%
4 310417
 
9.2%
5 196032
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3377568
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 844392
25.0%
0 844392
25.0%
1 844392
25.0%
3 337943
10.0%
4 310417
 
9.2%
5 196032
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3377568
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 844392
25.0%
0 844392
25.0%
1 844392
25.0%
3 337943
10.0%
4 310417
 
9.2%
5 196032
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3377568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 844392
25.0%
0 844392
25.0%
1 844392
25.0%
3 337943
10.0%
4 310417
 
9.2%
5 196032
 
5.8%

Month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8457375
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-01-22T18:06:57.905698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3239313
Coefficient of variation (CV)0.56860768
Kurtosis-1.0331705
Mean5.8457375
Median Absolute Deviation (MAD)3
Skewness0.2577004
Sum4936094
Variance11.048519
MonotonicityNot monotonic
2024-01-22T18:06:58.049275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 86343
10.2%
3 85980
10.2%
7 85587
10.1%
6 82576
9.8%
4 81731
9.7%
2 80243
9.5%
5 80103
9.5%
8 54413
6.4%
10 53292
6.3%
9 52330
6.2%
Other values (2) 101794
12.1%
ValueCountFrequency (%)
1 86343
10.2%
2 80243
9.5%
3 85980
10.2%
4 81731
9.7%
5 80103
9.5%
6 82576
9.8%
7 85587
10.1%
8 54413
6.4%
9 52330
6.2%
10 53292
6.3%
ValueCountFrequency (%)
12 50393
6.0%
11 51401
6.1%
10 53292
6.3%
9 52330
6.2%
8 54413
6.4%
7 85587
10.1%
6 82576
9.8%
5 80103
9.5%
4 81731
9.7%
3 85980
10.2%

Day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.835683
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-01-22T18:06:58.210921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.683456
Coefficient of variation (CV)0.54834743
Kurtosis-1.1796906
Mean15.835683
Median Absolute Deviation (MAD)7
Skewness0.011117124
Sum13371524
Variance75.402409
MonotonicityNot monotonic
2024-01-22T18:06:58.370862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
11 30121
 
3.6%
4 29474
 
3.5%
27 29270
 
3.5%
13 29262
 
3.5%
23 29241
 
3.5%
2 29235
 
3.5%
16 29203
 
3.5%
18 29060
 
3.4%
28 28367
 
3.4%
7 28359
 
3.4%
Other values (21) 552800
65.5%
ValueCountFrequency (%)
1 19368
2.3%
2 29235
3.5%
3 25058
3.0%
4 29474
3.5%
5 28176
3.3%
6 27566
3.3%
7 28359
3.4%
8 27961
3.3%
9 27068
3.2%
10 28159
3.3%
ValueCountFrequency (%)
31 15924
1.9%
30 26326
3.1%
29 23575
2.8%
28 28367
3.4%
27 29270
3.5%
26 26168
3.1%
25 27065
3.2%
24 28163
3.3%
23 29241
3.5%
22 27988
3.3%

WeekOfYear
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.646801
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 MiB
2024-01-22T18:06:58.563897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median23
Q335
95-th percentile49
Maximum52
Range51
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.389785
Coefficient of variation (CV)0.60852987
Kurtosis-1.0257391
Mean23.646801
Median Absolute Deviation (MAD)12
Skewness0.26228278
Sum19967170
Variance207.06591
MonotonicityNot monotonic
2024-01-22T18:06:58.782968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 20121
 
2.4%
12 20099
 
2.4%
9 20093
 
2.4%
11 20081
 
2.4%
6 20068
 
2.4%
5 20065
 
2.4%
8 20053
 
2.4%
10 20051
 
2.4%
4 20047
 
2.4%
3 20043
 
2.4%
Other values (42) 643671
76.2%
ValueCountFrequency (%)
1 15161
1.8%
2 19448
2.3%
3 20043
2.4%
4 20047
2.4%
5 20065
2.4%
6 20068
2.4%
7 20041
2.4%
8 20053
2.4%
9 20093
2.4%
10 20051
2.4%
ValueCountFrequency (%)
52 8319
1.0%
51 12355
1.5%
50 12333
1.5%
49 12334
1.5%
48 12334
1.5%
47 12182
1.4%
46 12333
1.5%
45 12334
1.5%
44 11042
1.3%
43 12361
1.5%

CompetitionOpen
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct336
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.953548
Minimum0
Maximum1386
Zeros343310
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-01-22T18:06:58.979568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median16
Q373
95-th percentile143
Maximum1386
Range1386
Interquartile range (IQR)73

Descriptive statistics

Standard deviation65.189741
Coefficient of variation (CV)1.5538553
Kurtosis131.80386
Mean41.953548
Median Absolute Deviation (MAD)16
Skewness7.548298
Sum35425240
Variance4249.7024
MonotonicityNot monotonic
2024-01-22T18:06:59.161615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 343310
40.7%
7 5369
 
0.6%
4 5361
 
0.6%
6 5323
 
0.6%
10 5255
 
0.6%
8 5247
 
0.6%
5 5211
 
0.6%
9 5201
 
0.6%
11 5119
 
0.6%
12 5004
 
0.6%
Other values (326) 453992
53.8%
ValueCountFrequency (%)
0 343310
40.7%
1 4482
 
0.5%
2 4779
 
0.6%
3 4680
 
0.6%
4 5361
 
0.6%
5 5211
 
0.6%
6 5323
 
0.6%
7 5369
 
0.6%
8 5247
 
0.6%
9 5201
 
0.6%
ValueCountFrequency (%)
1386 27
< 0.1%
1385 25
< 0.1%
1384 23
< 0.1%
1383 24
< 0.1%
1382 26
< 0.1%
1381 24
< 0.1%
1380 25
< 0.1%
1373 23
< 0.1%
1372 25
< 0.1%
1371 24
< 0.1%

Promo2Open
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct566
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.04371
Minimum0
Maximum72
Zeros481933
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-01-22T18:06:59.384253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324.918033
95-th percentile52.819672
Maximum72
Range72
Interquartile range (IQR)24.918033

Descriptive statistics

Standard deviation18.987353
Coefficient of variation (CV)1.4556712
Kurtosis0.2345155
Mean13.04371
Median Absolute Deviation (MAD)0
Skewness1.2349221
Sum11014004
Variance360.51958
MonotonicityNot monotonic
2024-01-22T18:06:59.583049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 481933
57.1%
23.54098361 1681
 
0.2%
23.31147541 1652
 
0.2%
24 1619
 
0.2%
23.7704918 1608
 
0.2%
24.2295082 1599
 
0.2%
24.45901639 1598
 
0.2%
24.68852459 1589
 
0.2%
22.39344262 1521
 
0.2%
25.37704918 1504
 
0.2%
Other values (556) 348088
41.2%
ValueCountFrequency (%)
0 481933
57.1%
0.2295081967 1055
 
0.1%
0.4590163934 1048
 
0.1%
0.6885245902 1045
 
0.1%
0.9180327869 1019
 
0.1%
0.9836065574 2
 
< 0.1%
1.147540984 1057
 
0.1%
1.213114754 31
 
< 0.1%
1.37704918 1020
 
0.1%
1.442622951 31
 
< 0.1%
ValueCountFrequency (%)
72 35
 
< 0.1%
71.7704918 42
 
< 0.1%
71.54098361 42
 
< 0.1%
71.31147541 42
 
< 0.1%
71.08196721 42
 
< 0.1%
70.85245902 42
 
< 0.1%
70.62295082 217
< 0.1%
70.39344262 252
< 0.1%
70.16393443 251
< 0.1%
69.93442623 251
< 0.1%

IsPromo2Month
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.1 MiB
0
718660 
1
125732 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters844392
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 718660
85.1%
1 125732
 
14.9%

Length

2024-01-22T18:06:59.798524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-22T18:06:59.970889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 718660
85.1%
1 125732
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 718660
85.1%
1 125732
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 844392
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 718660
85.1%
1 125732
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 844392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 718660
85.1%
1 125732
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 844392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 718660
85.1%
1 125732
 
14.9%

Interactions

2024-01-22T18:06:32.432986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:10.438734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:15.951996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:21.242037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:28.667256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:34.251053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:39.363901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:45.140807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:51.810087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:57.171078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:02.798803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:09.969923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:16.462714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:24.051302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:32.994526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:11.060558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:16.407540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:21.695541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:29.321516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:34.666982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:39.725624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:45.620745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:52.220975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:57.800719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:03.373865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:10.420793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:17.025422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:24.998729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:33.600688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:11.469741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:16.846526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:22.192484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:29.972858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:35.033778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:40.177597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:46.126481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:52.635340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:58.174476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:03.974311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:10.893812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:17.596621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:25.662416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:34.240917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:11.873296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:17.225669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:22.929860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:30.442225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:35.389108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:40.609915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:46.651028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:53.044355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:58.485275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:04.695351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:11.343074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:18.156222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:26.275210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:34.877836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:12.287746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:17.631924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:23.501081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:30.852365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:35.736257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:41.128250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:47.141178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:53.434460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:58.817291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:05.432274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:11.827152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:18.757325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:26.931018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:35.383281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:12.663728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:17.946835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:23.948912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:31.151550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:36.024804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:41.544890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:47.651496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:53.746250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:59.124850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:05.985856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:12.215134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:19.260796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:27.426592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:35.866142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:12.999132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:18.262321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:24.425318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:31.436657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:36.333838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:41.961443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:48.121771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:54.063589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:59.420823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:06.472021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:12.631264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:19.719658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:27.902920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:36.276940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:13.282429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:18.567448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:24.852730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:31.683247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:36.642159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:42.271513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:48.490726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:54.470769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:59.834361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:06.817427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:12.978029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:20.118968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:28.308382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:36.679532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:13.554019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:18.841480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:25.225228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:31.906250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:36.992766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:42.554205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:48.825831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:54.830604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:00.192688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:07.146109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:13.317592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:20.497516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:28.710718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:37.264765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:13.950931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:19.230541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:25.774249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:32.260655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:37.341180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:42.922314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:49.303692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:55.220935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:00.579310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:07.595794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:13.846481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:21.101450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:29.305454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:37.818803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:14.341119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:19.612298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:26.330152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:32.616153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:37.689322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:43.321064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:49.774672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:55.615993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:00.954970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:08.033530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:14.329571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:21.673991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:30.010736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:38.406142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:14.773921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:20.017426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:26.950575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:33.068990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:38.066717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:43.734355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:50.314098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:56.031554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:01.347111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:08.504982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:14.890154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:22.289529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:30.719088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:38.991131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:15.173324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:20.398183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:27.503964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:33.506768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:38.628100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:44.173555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:50.804406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:56.385716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:01.729162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:09.029110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:15.399480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:22.882958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:31.297945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:39.571765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:15.562092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:20.810644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:28.080372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:33.885096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:39.038552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:44.639483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:51.299074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:05:56.782951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:02.186191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:09.498255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:15.921945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:23.488362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-22T18:06:31.869231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2024-01-22T18:07:00.177829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
StoreDayOfWeekSalesCustomersCompetitionDistanceCompetitionOpenSinceMonthCompetitionOpenSinceYearPromo2SinceWeekPromo2SinceYearMonthDayWeekOfYearCompetitionOpenPromo2OpenPromoStateHolidaySchoolHolidayStoreTypeAssortmentPromo2PromoIntervalYearIsPromo2Month
Store1.0000.0000.0010.031-0.046-0.0520.0030.0070.0340.001-0.0000.001-0.005-0.0070.0000.0070.0000.0980.1150.0720.1600.0050.041
DayOfWeek0.0001.000-0.179-0.146-0.0000.0000.001-0.0010.003-0.0190.008-0.015-0.002-0.0040.4140.0260.2040.1680.1490.0290.0070.0070.016
Sales0.001-0.1791.0000.832-0.035-0.0420.0550.097-0.0340.062-0.0650.062-0.024-0.0810.3700.0550.0380.1120.0940.1190.0510.0350.062
Customers0.031-0.1460.8321.000-0.257-0.0320.0540.0380.0450.054-0.0470.055-0.015-0.2050.2080.0670.0230.3280.2720.2070.0480.0190.101
CompetitionDistance-0.046-0.000-0.035-0.2571.000-0.034-0.002-0.020-0.089-0.000-0.000-0.000-0.005-0.0470.0040.0130.0040.1620.1240.1600.0680.0030.067
CompetitionOpenSinceMonth-0.0520.000-0.042-0.032-0.0341.000-0.124-0.0390.045-0.0010.000-0.0010.057-0.0180.0000.0100.0000.1200.1050.1560.1920.0100.053
CompetitionOpenSinceYear0.0030.0010.0550.054-0.002-0.1241.000-0.0010.1020.001-0.0000.001-0.978-0.0850.0000.0030.0010.0650.1120.0840.1300.0060.043
Promo2SinceWeek0.007-0.0010.0970.038-0.020-0.039-0.0011.000-0.216-0.0280.001-0.026-0.0760.0790.0000.0140.0040.1540.2101.0000.5960.0290.076
Promo2SinceYear0.0340.003-0.0340.045-0.0890.0450.102-0.2161.000-0.0080.001-0.0070.010-0.8980.0000.0120.0060.1270.1931.0000.3010.0290.168
Month0.001-0.0190.0620.054-0.000-0.0010.001-0.028-0.0081.000-0.0060.9630.010-0.0020.0410.0350.4110.0070.0060.0290.0200.2630.187
Day-0.0000.008-0.065-0.047-0.0000.000-0.0000.0010.001-0.0061.0000.0470.0010.0050.3150.0220.1400.0000.0000.0000.0000.0160.026
WeekOfYear0.001-0.0150.0620.055-0.000-0.0010.001-0.026-0.0070.9630.0471.0000.0100.0040.1180.0340.3850.0070.0060.0280.0190.2550.145
CompetitionOpen-0.005-0.002-0.024-0.015-0.0050.057-0.978-0.0760.0100.0100.0010.0101.000-0.0180.0000.0020.0020.0500.0780.0610.0890.0460.034
Promo2Open-0.007-0.004-0.081-0.205-0.047-0.018-0.0850.079-0.898-0.0020.0050.004-0.0181.0000.0120.0060.0230.0770.0860.7900.1330.2140.433
Promo0.0000.4140.3700.2080.0040.0000.0000.0000.0000.0410.3150.1180.0000.0121.0000.0110.0290.0180.0130.0000.0000.0240.005
StateHoliday0.0070.0260.0550.0670.0130.0100.0030.0140.0120.0350.0220.0340.0020.0060.0111.0000.0320.0710.0680.0100.0040.0040.006
SchoolHoliday0.0000.2040.0380.0230.0040.0000.0010.0040.0060.4110.1400.3850.0020.0230.0290.0321.0000.0050.0040.0080.0000.0450.018
StoreType0.0980.1680.1120.3280.1620.1200.0650.1540.1270.0070.0000.0070.0500.0770.0180.0710.0051.0000.5380.1080.0720.0100.055
Assortment0.1150.1490.0940.2720.1240.1050.1120.2100.1930.0060.0000.0060.0780.0860.0130.0680.0040.5381.0000.0160.0860.0070.016
Promo20.0720.0290.1190.2070.1600.1560.0841.0001.0000.0290.0000.0280.0610.7900.0000.0100.0080.1080.0161.0001.0000.0310.419
PromoInterval0.1600.0070.0510.0480.0680.1920.1300.5960.3010.0200.0000.0190.0890.1330.0000.0040.0000.0720.0861.0001.0000.0220.033
Year0.0050.0070.0350.0190.0030.0100.0060.0290.0290.2630.0160.2550.0460.2140.0240.0040.0450.0100.0070.0310.0221.0000.065
IsPromo2Month0.0410.0160.0620.1010.0670.0530.0430.0760.1680.1870.0260.1450.0340.4330.0050.0060.0180.0550.0160.4190.0330.0651.000

Missing values

2024-01-22T18:06:40.883288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-22T18:06:43.740930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-01-22T18:06:49.241047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

StoreDayOfWeekDateSalesCustomersOpenPromoStateHolidaySchoolHolidayStoreTypeAssortmentCompetitionDistanceCompetitionOpenSinceMonthCompetitionOpenSinceYearPromo2Promo2SinceWeekPromo2SinceYearPromoIntervalYearMonthDayWeekOfYearCompetitionOpenPromo2OpenIsPromo2Month
0152015-07-31526355511NotHoliday1ca1270.09.02008.00NaNNaNNaN20157313182.00.0000000
1252015-07-31606462511NotHoliday1aa570.011.02007.0113.02010.0Jan,Apr,Jul,Oct20157313192.064.1311481
2352015-07-31831482111NotHoliday1aa14130.012.02006.0114.02011.0Jan,Apr,Jul,Oct201573131103.051.9016391
3452015-07-3113995149811NotHoliday1cc620.09.02009.00NaNNaNNaN20157313170.00.0000000
4552015-07-31482255911NotHoliday1aa29910.04.02015.00NaNNaNNaN2015731313.00.0000000
5652015-07-31565158911NotHoliday1aa310.012.02013.00NaNNaNNaN20157313119.00.0000000
6752015-07-3115344141411NotHoliday1ac24000.04.02013.00NaNNaNNaN20157313127.00.0000000
7852015-07-31849283311NotHoliday1aa7520.010.02014.00NaNNaNNaN2015731319.00.0000000
8952015-07-31856568711NotHoliday1ac2030.08.02000.00NaNNaNNaN201573131179.00.0000000
91052015-07-31718568111NotHoliday1aa3160.09.02009.00NaNNaNNaN20157313170.00.0000000
StoreDayOfWeekDateSalesCustomersOpenPromoStateHolidaySchoolHolidayStoreTypeAssortmentCompetitionDistanceCompetitionOpenSinceMonthCompetitionOpenSinceYearPromo2Promo2SinceWeekPromo2SinceYearPromoIntervalYearMonthDayWeekOfYearCompetitionOpenPromo2OpenIsPromo2Month
101658849422013-01-01311352710a1ba1260.06.02011.00NaNNaNNaN201311119.00.0000000
101660651222013-01-01264662510a1bb590.0NaNNaN15.02013.0Mar,Jun,Sept,Dec20131110.00.0000000
101662453022013-01-01290753210a1ac18160.0NaNNaN0NaNNaNNaN20131110.00.0000000
101665656222013-01-018498167510a1bc1210.0NaNNaN0NaNNaNNaN20131110.00.0000000
101677067622013-01-01382177710a1bb1410.09.02008.00NaNNaNNaN201311152.00.0000000
101677668222013-01-01337556610a1ba150.09.02006.00NaNNaNNaN201311176.00.0000000
101682773322013-01-0110765237710a1bb860.010.01999.00NaNNaNNaN2013111159.00.0000000
101686376922013-01-015035124810a1bb840.0NaNNaN148.02012.0Jan,Apr,Jul,Oct20131110.01.2131151
101704294822013-01-014491103910a1bb1430.0NaNNaN0NaNNaNNaN20131110.00.0000000
1017190109722013-01-015961140510a1bb720.03.02002.00NaNNaNNaN2013111130.00.0000000